scipy
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In this article, we will take a look under the hood of scipy.stats, exploring five essential tricks to design high-performance, rigorous simulations using only NumPy and SciPy.
A Coding Implementation to Master GPU Computing with CuPy, Custom CUDA Kernels, Streams, Sparse Matrices, and Profiling
Comprehensive taxonomy of linear algebra algorithms with Python & Ruby implementations. Covers vector operations, eigenvalues, SVD, decompositions, and BLAS/LAPACK routines.
Learn to calculate and interpret five essential effect size measures with Python examples and clear guidance.
Generating random points inside a sphere of any dimension.
💫 Industrial-strength Natural Language Processing (NLP) in Python - explosion/spaCy
Learn when to use NumPy vs SciPy for statistical computing with practical examples and decision frameworks.
In this article, you will learn how to visualize skewness and kurtosis using Python.
In this article, we'll explore 10 Python one-liners that showcase the progression from basic statistical tests to sophisticated analyses.
In this tutorial, we’ll learn more about the Cauchy distribution, visualize its probability density function, and learn how to use it in Python.
A measure of correlation between discrete (categorical) variables
From Microwave Countdowns to Never-Ending Call Holds, with Python
This is an overview of all the command-line tools discussed in this book. This includes binary executables, interpreted scripts, and Z Shell builtins and keywords. For each command-line tool, the...
Learn how Kernel Density Estimation works and how you can adjust it to better handle bounded data, like age, height, or price
How should we choose between label, one-hot, and target encoding?
30 Python libraries to solve most AI problems, including GenAI, data videos, synthetization, model evaluation, computer vision and more.
Density Based Clustering (DeBaCl) Toolbox.
Python library for portfolio optimization built on top of scikit-learn - skfolio/skfolio
🐧 A list of awesome Linux softwares .
Shared via ChatGPT
Understand the ins and outs of hierarchical clustering, and how it applies to marketing campaign analysis in the banking industry.
Implementing variational inference from scratch
Running Falcon-7B in the cloud as a microservice
Learn what vector search is and the metrics pertinent to decide the distance (or similarity) between objects.
Basics of anomaly detection, its use-cases, and an implementation of simple yet powerful algorithm in Python
A hands-on dive into scalar quantization (integer quantization) and product quantization with Python.
There are various challenges in MLOps and model sharing, including, security and reproducibility. To tackle these for scikit-learn models, we've developed a new open-source library: skops. In this article, I will walk you through how it works and how to use it with an end-to-end example.
Wasserstein distance helps WGANs outperform vanilla GANs and VAEs. This post explains why so using some easy math.
Introduction to key elements of ML and Autoencoders: Embedding, Clustering, and Similarity.
How to use Python libraries like Open3D, PyVista, and Vedo for neighborhood analysis of point clouds and meshes through KD-Trees/Octrees
As a data analyst at Microsoft, I must investigate and understand time-series data every day. Besides looking at some key performance…
Updates in progress. Jupyter workbooks will be added as time allows. - bjpcjp/scikit-learn
Sourced from O'Reilly ebook of the same name.
A Quick Guide to The Weibull Analysis
W3Schools offers free online tutorials, references and exercises in all the major languages of the web. Covering popular subjects like HTML, CSS, JavaScript, Python, SQL, Java, and many, many more.
Sourced from O'Reilly ebook of the same name.
Sourced from O'Reilly ebook of the same name.
Sourced from O'Reilly ebook of the same name.
Beginner's tour of spaCy v2.0.
A hands-on introduction to video technology: image, video, codec (av1, vp9, h265) and more (ffmpeg encoding). Translations: 🇺🇸 🇨🇳 🇯🇵 🇮🇹 🇰🇷 🇷🇺 🇧🇷 🇪🇸 - leandromoreira/digital_video_introduction
How to Model random Processes with Distributions and Fit them to Observational Data
For a short description of the main highlights of the release, please refer to Release Highlights for scikit-learn 1.0. Legend for changelogs something big that you couldn’t do before., something t...
for beginners as well as advanced users
An examination of the software that powers modern Climate simulations.
How to identify and segregate specific blobs in your image
How do you apply convolution kernels to colored images?
Demystifying the inner workings of BFGS optimization
Entropy, cross-entropy, log loss, and KL divergence
As a part of the Data Science community, Geospatial data is one of the most crucial kind of data to work with. The applications are as…
An introduction to PyMC3 through a concrete example
Mathematical Background and Example
Use Python to set your path towards it.
A basic guide to using Python to fit non-linear functions to experimental data points
Berkeley CS61A Textbook
Finding relationships between different variables/ features in a dataset during a data analysis task is one of the key and fundemental…
In this tutorial, you will learn how to get started with your NVIDIA Jetson Nano, including installing Keras + TensorFlow, accessing the camera, and performing image classification and object detection.
PRML algorithms implemented in Python.
By popular demand, I’ve updated this article with the latest tutorials from the past 12 months. Check it out here
There are multiple ways to doing the same thing in Pandas, and that might make it troublesome for the beginner user.This post is about handling most of the data manipulation cases in Python using a straightforward, simple, and matter of fact way.
Scikit-Optimize, or skopt, is a simple and efficient library to minimize (very) expensive and noisy black-box functions. It implements several…
Nature Methods - This Perspective describes the development and capabilities of SciPy 1.0, an open source scientific computing library for the Python programming language.
This post is about various evaluation metrics and how and when to use them.
PySpark is a great language for performing exploratory data analysis at scale, building machine learning pipelines, and creating ETLs for…
Basic linear algebra can have a surprising influence on deep learning and machine learning. Learn what linear algebra is and how to use it.
In this tutorial you will learn how to use Keras, Mask R-CNN, and Deep Learning for instance segmentation (both with and without a GPU).
spaCy is a modern Python library for industrial-strength Natural Language Processing. In this free and interactive online course, you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.
A Python Library for Outlier and Anomaly Detection, Integrating Classical and Deep Learning Techniques - yzhao062/pyod
Being in the data science domain for quite some years, I have seen good Jupyter notebooks but also a lot of ugly. Notebooks can have the perfect balance between text, code and visualisations but how often do your notebooks rather get messy and incomprehensible after a while? Follow some simple best practices to work more efficiently with your notebooks.
A guided walkthrough of how to use the Prophet python library to solve a common forecasting problem.
An easy-to-use library for recommender systems.
Tensorflow (Python API) implementation of Deep Photo Style Transfer - LouieYang/deep-photo-styletransfer-tf
Scenarios, tutorials and demos for Autonomous Driving - microsoft/AutonomousDrivingCookbook
Traditional strategies for taming unstructured, textual data
Strategies for working with continuous, numerical data
Efficient topic modelling in Python
Numba is an open-source Python compiler from Anaconda that can compile Python code for high-performance execution on CUDA-capable GPUs or multicore CPUs.
The Kullback-Leibler divergence between two probability distributions is sometimes called a "distance," but it's not. Here's why.